Governments operate large-scale credit assistance programs to promote the entry and survival of small firms. We study their effects on welfare and competition by analyzing the Small Business Administration’s 7(a) program-the largest credit guarantee scheme in the United States. We develop and estimate a model of firm behavior with credit constraints using confidential Census microdata from the hotel industry, the program’s largest recipient. Counterfactual analysis reveals that the program expands credit supply to inefficiently credit-constrained firms. We estimate that 7(a) guarantees cost $24 million and raise total welfare in targeted hotel markets by $54 million. Of these gains, $31 million come from valuable firms that fail to operate without guarantees, and $23 million from enhanced competition that lowers prices and expands markets. To understand how targeting can improve program design, we examine alternative allocations of 7(a) awards. Directing funds to concentrated markets increases consumer welfare by promoting entry, while awards in larger, less concentrated markets primarily benefit hotels by reducing exit and borrowing costs.

Advertising is a central marketing tool, yet credible evidence on its direct effect on sales is limited. We propose an additional motive for heavy advertising in concentrated markets: entry deterrence. Using a merged dataset of U.S. television advertising and sales in the chocolate industry, we document reduced entry in categories with intensive advertising. Exploiting quasi-experimental variation from coarse spatial targeting, our reduced-form estimates show that (i) the short-run impact of a brand’s own ads on its sales is modest, but (ii) a 10% increase in rivals’ total advertising lowers a brand’s sales by 1.6%. Motivated by these facts, we develop a dynamic model of product entry and advertising that captures entry deterrence through advertisers' ability to reduce profits of potential entrants. We will estimate the model to quantify the share of advertising attributable to entry-deterrence motives and to evaluate counterfactual policies that limit advertising intensity and their welfare implications.

We study how restrictive labor laws affected firm dynamics in India. Using novel policy variation from an amendment to the Industrial Disputes Act, we show that the introduction of restrictive labor laws significantly altered firm production and increased exit. The effects are especially pronounced in labor-intensive industries, which have historically served as key stepping stones for employment and industrial upgrading. To quantify the aggregate impact, we develop a multi-sector general equilibrium model where labor regulations create a wedge between market wages and effective labor costs in regulated sectors. We show that higher labor adjustment costs reduce aggregate output through two channels: static misallocation of resources away from regulated sectors toward less productive unregulated activities, and dynamic effects through reduced capital accumulation. We use the model to quantify how these distortions affect sectoral reallocation and long-run growth.

To match volatile demand with fixed capacity, cloud computing platforms employ tiered reliability-offering discounted spot compute services from which users can be ''evicted'' (i.e., interrupted) with little warning when capacity tightens. We study this market design using proprietary data from a major cloud platform, exploiting a price experiment and the quasi-random nature of evictions to estimate a structural model. The price elasticity of demand is -0.5, and evictions persistently reduce usage by 40%, indicating a strong revealed preference for reliability. More usage increases eviction rates, consistent with congestion. We interpret these facts through a model where heterogeneous users choose the compute reliability for each workload, while learning about eviction risk through experience. On the supply side, evictions arise endogenously given fixed capacity. Preliminary counterfactual results suggest that tiered reliability provides nearly Pareto gains relative to simply allowing the market to clear through congestion.

We study how recommendation system quality affects product variety and welfare in digital platforms. In our model, a profit-maximizing platform balances user subscriptions against creator compensation costs. As recommendation algorithms improve, the platform can better target consumers, and generates a content portfolio catered to mainstream preferences rather than serving niche segments. Better recommendations reduce returns to variety as ''insurance'' against consumer mismatch, leading to decreased product diversity, increased market concentration, and welfare redistribution—mainstream consumers gain better matches while niche consumers lose access to variety. Using the universe of streams from a major music platform, we find evidence consistent with these predictions—improvements in recommendation quality coincide with declining content diversity and a stronger correlation between popularity and centrality in the content space.

This paper studies the removal of an urban highway and quantifies how this policy impacts nearby neighborhoods and residents of the city. Using a difference-in-differences event study, we find large and persistent local changes: within one year of closure, treated blocks gained 12.03 residents per block, about 12.6 percent relative to pre-removal means. Meanwhile, property values in treated areas rose by almost roughly 9.4 percent of the average treated property's assessed value. We also document suggestive evidence that highway traffic was diverted to substitute surface roads. To interpret these patterns and evaluate welfare impacts of the policy, we build a quantitative spatial model with endogenous traffic and congestion that incorporates heterogeneity by race and income. We adapt tools from the demand estimation literature to build a spatial demand model which allows for rich substitution patterns and more plausible measurement of welfare. In the observed equilibrium, only 5.3 percent of residence–workplace pairs face higher expected commuting costs, yet increases are concentrated: 83 percent of residential neighborhoods have at least one destination with higher costs, with the largest penalties near the removed segment. Overall, the removal raises local amenities that capitalize into prices, while increases in commuting costs are small.